All models are wrong, but some are useful is a common saying in statistics. It does not merely apply to statistics, however. It is general observation. Box (1976) wrote an influential article on the topic. He says that you make progress by successively making models followed by experiments, followed by more models, and then more experiments. And so forth. Importantly, a more sophisticated model might be worse and lead to mediocrity.
Since all models are wrong the scientist cannot obtain a “correct” one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.
Without conducting experiments, building prototypes, having failures, and engaging in reality, an intelligence can have thoughts but not results. It cannot think its way to solving the world’s problems.
Once you take for granted that all models are incorrect, the next question to ask is which models are useful, says Box:
Now it would be very remarkable if any system existing in the real world could be exactly represented by any simple model. However, cunningly chosen parsimonious models often do provide remarkably useful approximations. For example, the law PV = RT relating pressure P, volume V and temperature T of an “ideal” gas via a constant R is not exactly true for any real gas, but it frequently provides a useful approximation and furthermore its structure is informative since it springs from a physical view of the behavior of gas molecules. For such a model there is no need to ask the question “Is the model true?”. If “truth” is to be the “whole truth” the answer must be “No”. The only question of interest is “Is the model illuminating and useful?”.
How do you know if something is useful? By testing it out in the real world!
Why would all models be incorrect? Part of the answer has to do with the fact that pure logic, pure mathematics only works locally. It does not scale. It does not mean that pure logic is ‘bad’, only that its application is limited.
Programmers and other system designers are ‘complexity managers’. If you are working with very strict rules in a limited domain, you can make pure logic prevail. A programmer can prove that a given function is correct. However, at scale, all software, all laws, all processes, all theories, all constitutions are incorrect. You assess whether they are useful. You check that they are correct in the way you care about. You cannot run a country or a large business with logic alone. In a sense, pure logic does not scale. Too many people underestimate the forces that push us toward common law and away from top-down edicts.
If you are a programmer, you should therefore not seek to make your software flawless and perfect. You may end up with worse software in the process. It may become overengineered. To get good software, test it out in practice. See how well it meets the needs of your clients. Then revise it, again and again.
If you are doing research, you should not work from models alone. Rather, you should start from a model, test it out in a meaningful manner, refine it again (based on your experience) and so forth, in a virtuous circle.
Evidently, running a business has to follow the same paradigm. All business plans are wrong, some are useful. Start with a plan, but revise it at the end of the first day.
In whatever you do, focus on what works. Try to test out your ideas as much as possible. Adjust your models frequently.
Never, never accept an untested model no matter how smart and clever its authors are.
Credit: I am grateful to Peter Turney for links and ideas.
Further reading: Gödel’s loophole.